TraceSecure: Towards Privacy Preserving Contact Tracing

TL;DR

TraceSecure introduces two privacy-preserving contact tracing methods to protect user data, addressing privacy concerns in COVID-19 apps by utilizing message-based techniques and cryptographic approaches like secret sharing and homomorphic encryption.

Contribution

The paper proposes two novel privacy-preserving contact tracing solutions that enhance user data security compared to existing methods.

Findings

First solution improves privacy with message-based techniques.

Second solution employs secret sharing and homomorphic encryption.

Both methods aim to secure user data during contact tracing.

Abstract

Contact tracing is being widely employed to combat the spread of COVID-19. Many apps have been developed that allow for tracing to be done automatically based off location and interaction data generated by users. There are concerns, however, regarding the privacy and security of users data when using these apps. These concerns are paramount for users who contract the virus, as they are generally required to release all their data. Motivated by the need to protect users privacy we propose two solutions to this problem. Our first solution builds on current "message based" methods and our second leverages ideas from secret sharing and additively homomorphic encryption.